time series

# install.packages("UKgrid")
# install.packages("timetk")
pacman::p_load(tidyverse, lubridate, UKgrid, janitor, timetk)
options(scipen = 999)


# check the current time
time <- Sys.time()
time

## [1] "2022-12-13 21:50:04 EAT"

# check the current date
date <- Sys.Date()
date

## [1] "2022-12-13"

class(date)

## [1] "Date"

class(time)

## [1] "POSIXct" "POSIXt"

time_lt <- as.POSIXlt(time)
class(time_lt)

## [1] "POSIXlt" "POSIXt"

unclass(time)

## [1] 1670957405

class(unclass(time_lt))

## [1] "list"

search()

##  [1] ".GlobalEnv"        "package:timetk"    "package:janitor"  
##  [4] "package:UKgrid"    "package:lubridate" "package:forcats"  
##  [7] "package:stringr"   "package:dplyr"     "package:purrr"    
## [10] "package:readr"     "package:tidyr"     "package:tibble"   
## [13] "package:ggplot2"   "package:tidyverse" "package:stats"    
## [16] "package:graphics"  "package:grDevices" "package:utils"    
## [19] "package:datasets"  "package:methods"   "Autoloads"        
## [22] "package:base"

dates_df <- read_csv("https://raw.githubusercontent.com/PacktPublishing/Hands-On-Time-Series-Analysis-with-R/master/Chapter02/dates_formats.csv")

## Rows: 22 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Japanese_format, US_format, US_long_format, CA_mix_format, SA_mix_f...
## dbl (1): Excel_Numeric_Format
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(dates_df)

## Rows: 22
## Columns: 7
## $ Japanese_format      <chr> "2017/1/20", "2017/1/21", "2017/1/22", "2017/1/23…
## $ US_format            <chr> "1/20/2017", "1/21/2017", "1/22/2017", "1/23/2017…
## $ US_long_format       <chr> "Friday, January 20, 2017", "Saturday, January 21…
## $ CA_mix_format        <chr> "January 20, 2017", "January 21, 2017", "January …
## $ SA_mix_format        <chr> "20 January 2017", "21 January 2017", "22 January…
## $ NZ_format            <chr> "20/01/2017", "21/01/2017", "22/01/2017", "23/01/…
## $ Excel_Numeric_Format <dbl> 42755, 42756, 42757, 42758, 42759, 42760, 42761, …

class(dates_df)

## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

dates_df %>%
  mutate(Japanese_format = parse_date_time(Japanese_format, order = "ymd"))

Japanese_format	US_format	US_long_format	CA_mix_format	SA_mix_format	NZ_format	Excel_Numeric_Format
2017-01-20	1/20/2017	Friday, January 20, 2017	January 20, 2017	20 January 2017	20/01/2017	42755
2017-01-21	1/21/2017	Saturday, January 21, 2017	January 21, 2017	21 January 2017	21/01/2017	42756
2017-01-22	1/22/2017	Sunday, January 22, 2017	January 22, 2017	22 January 2017	22/01/2017	42757
2017-01-23	1/23/2017	Monday, January 23, 2017	January 23, 2017	23 January 2017	23/01/2017	42758
2017-01-24	1/24/2017	Tuesday, January 24, 2017	January 24, 2017	24 January 2017	24/01/2017	42759
2017-01-25	1/25/2017	Wednesday, January 25, 2017	January 25, 2017	25 January 2017	25/01/2017	42760
2017-01-26	1/26/2017	Thursday, January 26, 2017	January 26, 2017	26 January 2017	26/01/2017	42761
2017-01-27	1/27/2017	Friday, January 27, 2017	January 27, 2017	27 January 2017	27/01/2017	42762
2017-01-28	1/28/2017	Saturday, January 28, 2017	January 28, 2017	28 January 2017	28/01/2017	42763
2017-01-29	1/29/2017	Sunday, January 29, 2017	January 29, 2017	29 January 2017	29/01/2017	42764
2017-01-30	1/30/2017	Monday, January 30, 2017	January 30, 2017	30 January 2017	30/01/2017	42765
2017-01-31	1/31/2017	Tuesday, January 31, 2017	January 31, 2017	31 January 2017	31/01/2017	42766
2017-02-01	2/1/2017	Wednesday, February 1, 2017	February 1, 2017	01 February 2017	1/2/2017	42767
2017-02-02	2/2/2017	Thursday, February 2, 2017	February 2, 2017	02 February 2017	2/2/2017	42768
2017-02-03	2/3/2017	Friday, February 3, 2017	February 3, 2017	03 February 2017	3/2/2017	42769
2017-02-04	2/4/2017	Saturday, February 4, 2017	February 4, 2017	04 February 2017	4/2/2017	42770
2017-02-05	2/5/2017	Sunday, February 5, 2017	February 5, 2017	05 February 2017	5/2/2017	42771
2017-02-06	2/6/2017	Monday, February 6, 2017	February 6, 2017	06 February 2017	6/2/2017	42772
2017-02-07	2/7/2017	Tuesday, February 7, 2017	February 7, 2017	07 February 2017	7/2/2017	42773
2017-02-08	2/8/2017	Wednesday, February 8, 2017	February 8, 2017	08 February 2017	8/2/2017	42774
2017-02-09	2/9/2017	Thursday, February 9, 2017	February 9, 2017	09 February 2017	9/2/2017	42775
2017-02-10	2/10/2017	Friday, February 10, 2017	February 10, 2017	10 February 2017	10/2/2017	42776

dates_df %>%
  mutate(
    Japanese_format = ymd(Japanese_format),
    US_format = mdy(US_format),
    US_long_format = mdy(US_long_format),
    SA_mix_format = dmy(SA_mix_format)
  ) %>%
  view()

time_US_str <- "Monday, December 31, 2018 11:59:59 PM"

mdy_hms(time_US_str, tz = "Africa/Nairobi")

## [1] "2018-12-31 23:59:59 EAT"

view(UKgrid)
rm(UKgrid)

## Warning in rm(UKgrid): object 'UKgrid' not found

uk_data <-
  UKgrid %>%
  # lowercase all column names
  clean_names() %>%
  select(timestamp, nd)



year(uk_data$timestamp)[1:5]

## [1] 2005 2005 2005 2005 2005

month(uk_data$timestamp, label = T)[1:5]

## [1] Apr Apr Apr Apr Apr
## 12 Levels: Jan < Feb < Mar < Apr < May < Jun < Jul < Aug < Sep < ... < Dec

uk_data %>%
  mutate(month = month(timestamp, label = T)) %>%
  tail()

	timestamp	nd	month
254587	2019-10-08 21:00:00	31151	Oct
254588	2019-10-08 21:30:00	29294	Oct
254589	2019-10-08 22:00:00	27308	Oct
254590	2019-10-08 22:30:00	25422	Oct
254591	2019-10-08 23:00:00	23487	Oct
254592	2019-10-08 23:30:00	21974	Oct

# we can group_by() the year and month to aggregate to a monthly frequency.
uk_data %>%
  group_by(year(timestamp), month(timestamp, label = T)) %>%
  summarise(nd = sum(nd))

## `summarise()` has grouped output by 'year(timestamp)'. You can override using
## the `.groups` argument.

year(timestamp)	month(timestamp, label = T)	nd
2005	Apr	55860950
2005	May	53290225
2005	Jun	50384699
2005	Jul	50821971
2005	Aug	50932241
2005	Sep	51640229
2005	Oct	56185094
2005	Nov	62108220
2005	Dec	65248152
2006	Jan	66849497
2006	Feb	60579739
2006	Mar	NA
2006	Apr	54663527
2006	May	53186630
2006	Jun	49795953
2006	Jul	51547742
2006	Aug	50713472
2006	Sep	51215300
2006	Oct	55663834
2006	Nov	59846855
2006	Dec	62140504
2007	Jan	63732161
2007	Feb	57337349
2007	Mar	NA
2007	Apr	51450615
2007	May	52325269
2007	Jun	49619310
2007	Jul	50586453
2007	Aug	50375549
2007	Sep	50934675
2007	Oct	57333540
2007	Nov	60162398
2007	Dec	63153689
2008	Jan	63890011
2008	Feb	59346543
2008	Mar	NA
2008	Apr	55971856
2008	May	51235828
2008	Jun	48708372
2008	Jul	50252201
2008	Aug	49106426
2008	Sep	50424585
2008	Oct	55383466
2008	Nov	57658081
2008	Dec	61310313
2009	Jan	63238386
2009	Feb	56071814
2009	Mar	NA
2009	Apr	49312584
2009	May	48428307
2009	Jun	46902613
2009	Jul	47749333
2009	Aug	46796255
2009	Sep	47432398
2009	Oct	52111312
2009	Nov	54089976
2009	Dec	60551151
2010	Jan	64161466
2010	Feb	56806305
2010	Mar	NA
2010	Apr	49842384
2010	May	49267519
2010	Jun	46469416
2010	Jul	47404090
2010	Aug	46201165
2010	Sep	47581174
2010	Oct	52365421
2010	Nov	57183714
2010	Dec	65016325
2011	Jan	61790902
2011	Feb	53644420
2011	Mar	NA
2011	Apr	47332419
2011	May	47606475
2011	Jun	46423796
2011	Jul	46637732
2011	Aug	46887222
2011	Sep	47023328
2011	Oct	50414498
2011	Nov	53419646
2011	Dec	57166874
2012	Jan	58518689
2012	Feb	56589617
2012	Mar	NA
2012	Apr	50344841
2012	May	49806625
2012	Jun	45197770
2012	Jul	46521301
2012	Aug	46153913
2012	Sep	45943095
2012	Oct	52176933
2012	Nov	54836483
2012	Dec	57666386
2013	Jan	60083628
2013	Feb	54298601
2013	Mar	NA
2013	Apr	50870393
2013	May	47325312
2013	Jun	43811218
2013	Jul	46156155
2013	Aug	44900214
2013	Sep	46089789
2013	Oct	49737150
2013	Nov	54003270
2013	Dec	54754913
2014	Jan	56909093
2014	Feb	50388456
2014	Mar	NA
2014	Apr	46387968
2014	May	45609432
2014	Jun	43056191
2014	Jul	44616054
2014	Aug	42917349
2014	Sep	44808525
2014	Oct	48141164
2014	Nov	51342735
2014	Dec	54481603
2015	Jan	56481442
2015	Feb	51734473
2015	Mar	NA
2015	Apr	44697912
2015	May	43387305
2015	Jun	41306952
2015	Jul	42292826
2015	Aug	41883601
2015	Sep	43182545
2015	Oct	48303656
2015	Nov	49263983
2015	Dec	49323073
2016	Jan	53584912
2016	Feb	50024141
2016	Mar	NA
2016	Apr	45057811
2016	May	41471080
2016	Jun	40653586
2016	Jul	40197282
2016	Aug	39749783
2016	Sep	40629190
2016	Oct	45825859
2016	Nov	51077791
2016	Dec	51575801
2017	Jan	55101622
2017	Feb	47109847
2017	Mar	NA
2017	Apr	40943010
2017	May	41025654
2017	Jun	37999934
2017	Jul	39033334
2017	Aug	38891221
2017	Sep	40287507
2017	Oct	43429259
2017	Nov	48595772
2017	Dec	51724980
2018	Jan	52607241
2018	Feb	48256605
2018	Mar	NA
2018	Apr	42945982
2018	May	39424987
2018	Jun	37396587
2018	Jul	39412416
2018	Aug	38957886
2018	Sep	38626177
2018	Oct	43913596
2018	Nov	47387903
2018	Dec	49016945
2019	Jan	52716003
2019	Feb	44499966
2019	Mar	NA
2019	Apr	41082917
2019	May	39777740
2019	Jun	36927053
2019	Jul	38304773
2019	Aug	36778929
2019	Sep	37547370
2019	Oct	10905359

# That does what we want, but it also separates the year and month
# into separate columns and converts them non-DateTime objects.
# There’s a better way! The timetk package provides
# a summarize_by_time() function that does exactly what we want.

uk_monthly_data <- uk_data %>%
  summarize_by_time(
    .date_var = timestamp,
    .by = "month",
    nd = sum(nd, na.rm = TRUE)
  )

# As a final step, let’s also restrict our data to fall between 2006
# and 2018. Again, we could do this with filter(year(timestamp) >= 2006 &
# year(timestamp) <= 2018) but then we would run into the same problems as
# with the summarize() function. Instead, we’ll use filter_by_time():

uk_monthly_data %>%
  filter_by_time(
    .date_var = timestamp,
    .start_date = "2006",
    .end_date = "2018"
  )

timestamp	nd
2006-01-01	66849497
2006-02-01	60579739
2006-03-01	65731906
2006-04-01	54663527
2006-05-01	53186630
2006-06-01	49795953
2006-07-01	51547742
2006-08-01	50713472
2006-09-01	51215300
2006-10-01	55663834
2006-11-01	59846855
2006-12-01	62140504
2007-01-01	63732161
2007-02-01	57337349
2007-03-01	60420637
2007-04-01	51450615
2007-05-01	52325269
2007-06-01	49619310
2007-07-01	50586453
2007-08-01	50375549
2007-09-01	50934675
2007-10-01	57333540
2007-11-01	60162398
2007-12-01	63153689
2008-01-01	63890011
2008-02-01	59346543
2008-03-01	60473995
2008-04-01	55971856
2008-05-01	51235828
2008-06-01	48708372
2008-07-01	50252201
2008-08-01	49106426
2008-09-01	50424585
2008-10-01	55383466
2008-11-01	57658081
2008-12-01	61310313
2009-01-01	63238386
2009-02-01	56071814
2009-03-01	56518546
2009-04-01	49312584
2009-05-01	48428307
2009-06-01	46902613
2009-07-01	47749333
2009-08-01	46796255
2009-09-01	47432398
2009-10-01	52111312
2009-11-01	54089976
2009-12-01	60551151
2010-01-01	64161466
2010-02-01	56806305
2010-03-01	57716147
2010-04-01	49842384
2010-05-01	49267519
2010-06-01	46469416
2010-07-01	47404090
2010-08-01	46201165
2010-09-01	47581174
2010-10-01	52365421
2010-11-01	57183714
2010-12-01	65016325
2011-01-01	61790902
2011-02-01	53644420
2011-03-01	57306818
2011-04-01	47332419
2011-05-01	47606475
2011-06-01	46423796
2011-07-01	46637732
2011-08-01	46887222
2011-09-01	47023328
2011-10-01	50414498
2011-11-01	53419646
2011-12-01	57166874
2012-01-01	58518689
2012-02-01	56589617
2012-03-01	54141450
2012-04-01	50344841
2012-05-01	49806625
2012-06-01	45197770
2012-07-01	46521301
2012-08-01	46153913
2012-09-01	45943095
2012-10-01	52176933
2012-11-01	54836483
2012-12-01	57666386
2013-01-01	60083628
2013-02-01	54298601
2013-03-01	58907274
2013-04-01	50870393
2013-05-01	47325312
2013-06-01	43811218
2013-07-01	46156155
2013-08-01	44900214
2013-09-01	46089789
2013-10-01	49737150
2013-11-01	54003270
2013-12-01	54754913
2014-01-01	56909093
2014-02-01	50388456
2014-03-01	52260609
2014-04-01	46387968
2014-05-01	45609432
2014-06-01	43056191
2014-07-01	44616054
2014-08-01	42917349
2014-09-01	44808525
2014-10-01	48141164
2014-11-01	51342735
2014-12-01	54481603
2015-01-01	56481442
2015-02-01	51734473
2015-03-01	52456541
2015-04-01	44697912
2015-05-01	43387305
2015-06-01	41306952
2015-07-01	42292826
2015-08-01	41883601
2015-09-01	43182545
2015-10-01	48303656
2015-11-01	49263983
2015-12-01	49323073
2016-01-01	53584912
2016-02-01	50024141
2016-03-01	51083983
2016-04-01	45057811
2016-05-01	41471080
2016-06-01	40653586
2016-07-01	40197282
2016-08-01	39749783
2016-09-01	40629190
2016-10-01	45825859
2016-11-01	51077791
2016-12-01	51575801
2017-01-01	55101622
2017-02-01	47109847
2017-03-01	47652508
2017-04-01	40943010
2017-05-01	41025654
2017-06-01	37999934
2017-07-01	39033334
2017-08-01	38891221
2017-09-01	40287507
2017-10-01	43429259
2017-11-01	48595772
2017-12-01	51724980
2018-01-01	52607241
2018-02-01	48256605
2018-03-01	51572809
2018-04-01	42945982
2018-05-01	39424987
2018-06-01	37396587
2018-07-01	39412416
2018-08-01	38957886
2018-09-01	38626177
2018-10-01	43913596
2018-11-01	47387903
2018-12-01	49016945

head(uk_data)

timestamp	nd
2005-04-01 00:00:00	32926
2005-04-01 00:30:00	32154
2005-04-01 01:00:00	33633
2005-04-01 01:30:00	34574
2005-04-01 02:00:00	34720
2005-04-01 02:30:00	34452

glimpse(uk_data)

## Rows: 254,592
## Columns: 2
## $ timestamp <dttm> 2005-04-01 00:00:00, 2005-04-01 00:30:00, 2005-04-01 01:00:…
## $ nd        <int> 32926, 32154, 33633, 34574, 34720, 34452, 33818, 32951, 3244…

ggplot(data = uk_monthly_data, aes(x = timestamp, y = nd)) +
  geom_line() +
  geom_smooth(se = FALSE) +
  labs(x = "Monthly Data", y = "Electricity Demand") +
  scale_x_datetime(date_breaks = "year", date_labels = "%b-%Y") +
  theme(
    axis.text.x = element_text(angle = 90)
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

rm(time_lt, date, time)

plot_time_series(uk_monthly_data,
  .date_var = timestamp,
  .value = nd,
  .interactive = TRUE,
  .x_lab = "Monthy Data",
  .y_lab = "Electricity Demand"
)